{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DS4000 Rigol Waveform Examples\n",
    "\n",
    "**Scott Prahl**\n",
    "\n",
    "**March 2021**\n",
    "\n",
    "This notebook illustrates shows how to extract signals from a `.wfm` file created by a the Rigol DS4000 scope.  It also validates that the process works by comparing with `.csv` and screenshots.\n",
    "\n",
    "Two different `.wfm` files are examined one for the DS4022 scope and one for the DS4024 scope.  The accompanying `.csv` files seem to have t=0 in the zero in the center of the waveform. \n",
    "\n",
    "*If RigolWFM is not installed, uncomment the following cell (i.e., delete the #) and run (shift-enter)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --user RigolWFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    import RigolWFM.wfm as rigol\n",
    "except ModuleNotFoundError:\n",
    "    print('RigolWFM not installed. To install, uncomment and run the cell above.')\n",
    "    print('Once installation is successful, rerun this cell again.')\n",
    "\n",
    "repo = \"https://github.com/scottprahl/RigolWFM/raw/master/wfm/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list of Rigol scopes that should have the same file format is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['4', '4000', 'DS4000', 'DS4054', 'DS4052', 'DS4034', 'DS4032', 'DS4024', 'DS4022', 'DS4014', 'DS4012', 'MSO4054', 'MSO4052', 'MSO4034', 'MSO4032', 'MSO4024', 'MSO4022', 'MSO4014', 'MSO4012']\n"
     ]
    }
   ],
   "source": [
    "print(rigol.DS4000_scopes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS4022 Waveform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at a screen shot\n",
    "\n",
    "Start with a `.wfm` file from a Rigol DS4022 scope.  It should looks something like this\n",
    "\n",
    "<img src=\"https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS4022-A.png\" width=\"70%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the `.wfm` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS4022-A.wfm?raw=true'\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "name = \"DS4022-A.wfm\"\n",
    "wfm_filename = repo + name + \"?raw=true\"  \n",
    "\n",
    "w = rigol.Wfm.from_url(wfm_filename, '4000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First a textual description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    General:\n",
      "        File Model   = wfm4000\n",
      "        User Model   = 4000\n",
      "        Parser Model = wfm4000\n",
      "        Firmware     = 00.02.03.00.03\n",
      "        Filename     = DS4022-A.wfm\n",
      "        Channels     = [1, 2, 3, 4]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =       DC\n",
      "            Scale =    50.00  V/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =      10X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =    1.000 µs/div\n",
      "           Offset =  500.000 ns\n",
      "            Delta =    2.000 ns/point\n",
      "           Points =     7000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...      6999,     7000]\n",
      "           Raw    = [      215,      222,      227  ...        20,       20]\n",
      "           Times  = [-6.500 µs,-6.498 µs,-6.496 µs  ...  7.498 µs, 7.500 µs]\n",
      "           Volts  = [137.50  V,148.44  V,156.25  V  ... -167.19  V,-167.19  V]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =       DC\n",
      "            Scale =   200.00  V/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =     100X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =    1.000 µs/div\n",
      "           Offset =  500.000 ns\n",
      "            Delta =    2.000 ns/point\n",
      "           Points =     7000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...      6999,     7000]\n",
      "           Raw    = [      127,      127,      127  ...       127,      127]\n",
      "           Times  = [-6.500 µs,-6.498 µs,-6.496 µs  ...  7.498 µs, 7.500 µs]\n",
      "           Volts  = [ -0.00  V, -0.00  V, -0.00  V  ...  -0.00  V, -0.00  V]\n",
      "\n",
      "     Channel 3:\n",
      "         Coupling =       DC\n",
      "            Scale =    50.00  V/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =      10X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =    1.000 µs/div\n",
      "           Offset =  500.000 ns\n",
      "            Delta =    2.000 ns/point\n",
      "           Points =     7000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...      6999,     7000]\n",
      "           Raw    = [       65,       57,       49  ...       236,      236]\n",
      "           Times  = [-6.500 µs,-6.498 µs,-6.496 µs  ...  7.498 µs, 7.500 µs]\n",
      "           Volts  = [-96.88  V,-109.38  V,-121.88  V  ... 170.31  V,170.31  V]\n",
      "\n",
      "     Channel 4:\n",
      "         Coupling =       DC\n",
      "            Scale =   200.00  V/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =     100X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =    1.000 µs/div\n",
      "           Offset =  500.000 ns\n",
      "            Delta =    2.000 ns/point\n",
      "           Points =     7000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...      6999,     7000]\n",
      "           Raw    = [      127,      127,      127  ...       128,      127]\n",
      "           Times  = [-6.500 µs,-6.498 µs,-6.496 µs  ...  7.498 µs, 7.500 µs]\n",
      "           Volts  = [ -0.00  V, -0.00  V, -0.00  V  ...   6.25  V, -0.00  V]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "description = w.describe()\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the actual signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toff=0.05\n",
    "ch=w.channels[0]\n",
    "plt.title(\"CH%d %.2fV/div %.2fVoff (%s %s)\" % (1,ch.volt_per_division, ch.volt_offset, w.basename, w.firmware))\n",
    "plt.plot(ch.times*1e3,ch.volts, color='blue', label='WFM')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Voltage (V)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note that CH3 and CH4 are both effectively zero, just displaced in the display graph above\n",
    "w.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DS4024 Waveform\n",
    "\n",
    "### Start with importing the `.csv` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"DS4024-A.csv\"\n",
    "csv_filename = repo + filename\n",
    "\n",
    "csv_data = np.genfromtxt(csv_filename, delimiter=',', skip_header=2).T\n",
    "\n",
    "# need to do this separately because only the start and increment is given in the csv file\n",
    "time = csv_data[0] * 2.000000e-06 - 1.400000e-03\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the `.csv` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time*1000,csv_data[1], color='blue')\n",
    "plt.plot(time*1000,csv_data[2], color='red')\n",
    "plt.xlabel(\"time (ms)\")\n",
    "plt.ylabel(\"Volts (V)\")\n",
    "plt.title(\"DS4024-A\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the `.wfm` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading 'https://github.com/scottprahl/RigolWFM/raw/master/wfm/DS4024-A.wfm?raw=true'\n"
     ]
    }
   ],
   "source": [
    "# raw=true is needed because this is a binary file\n",
    "name = \"DS4024-A.wfm\"\n",
    "wfm_filename = repo + name + \"?raw=true\"  \n",
    "\n",
    "w = rigol.Wfm.from_url(wfm_filename, '4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now describe the `.wfm` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    General:\n",
      "        File Model   = wfm4000\n",
      "        User Model   = 4\n",
      "        Parser Model = wfm4000\n",
      "        Firmware     = 00.02.03.02.00\n",
      "        Filename     = DS4024-A.wfm\n",
      "        Channels     = [1, 2]\n",
      "\n",
      "     Channel 1:\n",
      "         Coupling =       DC\n",
      "            Scale =     1.00  V/div\n",
      "           Offset =  -576.00 mV\n",
      "            Probe =       1X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =  200.000 µs/div\n",
      "           Offset =  100.000 µs\n",
      "            Delta =    4.000 ns/point\n",
      "           Points =   700000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...    699999,   700000]\n",
      "           Raw    = [      109,      109,      109  ...       205,      206]\n",
      "           Times  = [-1.300 ms,-1.300 ms,-1.300 ms  ...  1.500 ms, 1.500 ms]\n",
      "           Volts  = [ 13.50 mV, 13.50 mV, 13.50 mV  ...   3.01  V,  3.04  V]\n",
      "\n",
      "     Channel 2:\n",
      "         Coupling =       DC\n",
      "            Scale =   200.00 mV/div\n",
      "           Offset =     0.00  V\n",
      "            Probe =       1X\n",
      "         Inverted =    False\n",
      "\n",
      "        Time Base =  200.000 µs/div\n",
      "           Offset =  100.000 µs\n",
      "            Delta =    4.000 ns/point\n",
      "           Points =   700000\n",
      "\n",
      "         Count    = [        1,        2,        3  ...    699999,   700000]\n",
      "           Raw    = [      126,      127,      127  ...       127,      127]\n",
      "           Times  = [-1.300 ms,-1.300 ms,-1.300 ms  ...  1.500 ms, 1.500 ms]\n",
      "           Volts  = [ -6.25 mV, -0.00  V, -0.00  V  ...  -0.00  V, -0.00  V]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(w.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally compare the `.wfm` data to the `.csv` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toff=0.05\n",
    "ch=w.channels[0]\n",
    "plt.title(\"CH%d %.2fV/div %.2fVoff (%s %s)\" % (1,ch.volt_per_division, ch.volt_offset, w.basename, w.firmware))\n",
    "plt.plot(ch.times*1e3,ch.volts, color='blue', label='WFM')\n",
    "\n",
    "plt.plot(time*1e3+toff,csv_data[1], color='red', label='CSV')\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Voltage (V)\")\n",
    "plt.xlim(-1.5,2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toff=0.0565\n",
    "\n",
    "ch=w.channels[1]\n",
    "plt.title(\"CH%d %.2fV/div %.2fVoff (%s %s)\" % (2,ch.volt_per_division, ch.volt_offset, w.basename, w.firmware))\n",
    "plt.plot(ch.times*1e3,ch.volts, color='blue', label='WFM')\n",
    "\n",
    "plt.plot(time*1e3+toff,csv_data[2], color='red', label='CSV')\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Voltage (V)\")\n",
    "plt.xlim(-0.5,1.0)\n",
    "#plt.ylim(-0.02,0.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "nteract": {
   "version": "0.15.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
